Critical gaps across all dimensions. Tool 1 (annotate_rlhf_preference_pair) has a reasonable description (168 chars) and three parameters with types and descriptions, meeting baseline minimums. Tool 2 (execute_skill_action) has NO visible schema, parameters, or description in the source code, only a name and risk classification. Neither tool has documented output schemas. Parameter descriptions lack constraints (enums, ranges, formats). No error handling guidance. No security considerations (e.g., how are rubric criteria validated?). The server is STDIO-only, which is a hard transport cap at 50. Overall definition quality is severely limited by missing schemas and incomplete parameter documentation.

• Add explicit input schema and description to execute_skill_action. Define all parameters (names, types, descriptions, constraints). Minimum 50 chars description explaining what action it executes and when to call it.
• Document output schemas for both tools as JSON Schema objects. Include all fields returned (e.g., annotation_batch_id, preferred_completion, inter_annotator_agreement_krippendorff_alpha, etc.) with types and descriptions.
• Convert rubric_criteria parameter to an enum with explicit allowed values: ['FACTUALITY_AND_CONCISENESS', 'CODE_SAFETY_AND_ROBUSTNESS', ...]. Remove example values from description; use formal constraint instead.
• Add error handling guidance: 'If rubric_criteria is invalid, returns error with list of valid options. If inter-annotator agreement < 0.7, returns warning but still creates DPO entry. If model completions exceed 10k chars, returns error with truncation guidance.'
• Rename execute_skill_action to a specific verb_noun pair (e.g., run_red_teaming_pipeline, generate_dpo_dataset, execute_annotation_batch) that clearly conveys the action and helps LLM select the right tool.